Practical Data Science with Python
Assignment 1: Data Cleaning and Summarising
Due: 23:59 on the 13th of April 2025
This assignment is worth 25% of your overall mark.
Introduction
In this assignment, you will examine a (set of) data file(s) and carry out the first steps of the data science process, including the cleaning and exploring of data. You will need to develop and implement appropriate steps, in Jupyter notebook (in the Anaconda version specified in the canvas announcement), to load a data file into memory, clean, process, and analyse it. This assignment is intended to give you practical experience with the typical first steps of the data science process.
The “Practical Data Science” Canvas contains further announcements and a discussion board for this assignment. Please be sure to check these on a regular basis – it is your responsibility to stay informed with regards to any announcements or changes. Login through https://rmit.instructure.com/.
Where to Develop Your Code
You are encouraged to develop and test your code in the following environments: Jupyter Notebook on Lab PCs. (Or please use the Anaconda version as specified in the course announcement)
Jupyter Notebook on Lab PCs
On Lab Computer, you can find Jupyter Notebook via:
Start → All Programs → Jupyter Notebook - Anaconda
Then,
• Select New → Python 3
• The new created ‘*.ipynd’ is created at the following location:
– C:\Users\sXXXXXXX
– where sXXXXXXX should be replaced with a string consisting of the letter “s” followed by your student number.
Plagiarism
RMIT University takes plagiarism very seriously. All assignments will be checked with plagiarism-detection software; any student found to have plagiarised will be subject to disciplinary action as described in the course guide. Plagiarism includes submitting code that is not your own or submitting text that is not your own. Allowing others to copy your work is also plagiarism. All plagiarism will be penalised; there are no exceptions and no excuses. For further information, please see the Academic Integrity information at http://www1.rmit.edu.au/academicintegrity.
Turnitin will be used for Plagiarism Review for this assignment in Canvas.
General Requirements
This section contains information about the general requirements that your assignment must meet. Please read all requirements carefully before you start.
• You must do the assignment in Jupiter Notebook that are available in Anaconda.
• Parts of this assignment will include a written report; this must be in PDF format.
• Please ensure that your submission follows the file naming rules specified in the tasks below. File names are case sensitive, i.e. if it is specified that the file name is gryphon, then that is exactly the file name you should submit; Gryphon, GRYPHON, griffin, and anything else but gryphon will be rejected.
Task 1: Data Preparation (8 marks)
Have a look at the file hotel_bookings_data.zip, which is available in Canvas under the Assignments/Assignment 1 section of the course Canvas. This dataset contains booking information for a city hotel and a resort hotel, including details such as booking dates, length of stay, number of guests, and more.
Being a careful data scientist, you know that it is vital to carefully check any available data before starting to analyse it. Your task is to prepare the provided data for analysis. You will start by loading the CSV data from the file (using appropriate pandas functions) and checking whether the loaded data is equivalent tothe data inthe source CSV file. Next, apply the data cleaning techniques covered in the lectures to resolve issues/errors, including handling missing data, eliminating duplicates, standardizing formats, and addressing outliers.
Hint: it is a good practice to check every column in the dataset for any potential data issues; however, it should be fine that you have identified and corrected data issues in 5 columns in the dataset.
Once the data is cleaned, store the refined version as a CSV file, e.g. (file can be named as ‘cleaned version.csv’).
Task 2: Data Exploration (10 marks)
Q1: What insights can be gained from exploring booking trends over time, such as monthly trend of booking statistics (e.g., total bookings, cancellations, average lead time) in the year of 2016?
Q2: How do we describe and visualize the complex relationships between bookings, seasons and years for data between 2015 and 2017? What can be learned from this analysis?
Q3: What stories emerge when we analyze the distribution of bookings by geographic region (like top 5 countries), and how do these insights inform. our understanding of their customer behavior. and preferences (e.g., average length of stay and cancellation rates)?
Note, each visualization (graph) should be complete and informative in itself and should be clear for readers to read and obtain information.
Task 3: Report (7 marks)
Write your report and save it in a file called report.pdf, and it must be in PDF format, and must be at most 6 (in single column format) pages (including figures and references) with a font size between 10 and 12 points. Penalties will apply if the report does not satisfy the requirement. Moreover, the quality of the report will be considered, e.g. clarity, grammar mistakes, the flow of the presentation.
Remember to clearly cite any sources (including books, research papers, course notes, etc.) that you referred to while designing aspects of your programs.
• Create a heading called “Data Preparation” in your report.
– Provide a brief explanation of how you addressed the task. For the steps of dealing with the potential issues/errors, please create a sub-section for each type of errors you dealt with (e.g. typos, extra whitespaces, sanity checks for impossible values, and missing values etc), and also explain and justify how you dealt with each kind of errors.
• Create a heading called “Data Exploration” in your report.
– For each numbered step in Task 2 above, create a sub-section with corresponding numbering. Please explain and justify how you explore the data as required properly.
What to Submit, When, and How
The assignment is due at
23:59 on the 13th of April 2025.
Assignments submitted after this time will be subject to standard late submission penalties.
You need to submit the following files:
– Notebook file containing your python commands for Task 1 and Task, ‘assignment1.ipynb’. Please use the provided solution template to organise your solutions: assignment1 TEMPLATE.ipynb
# For the notebook files, please make sure to clean them and remove any unnecessary lines of code (cells). Follow these steps before submission:
1. Main menu → Kernel → Restart & Run All
2. Wait till you see the output displayed properly. You should see all the dataprinted and graphs displayed.
– Your report.pdf file: at most 6 (in single column format) pages (including figures and references) with a font size between 10 and 12 points. Penalties will apply if the report does not satisfy the requirement.
They must be submitted as ONE single zip file, named as your student number (for example, 1234567.zip if your student ID is s1234567). The zip file must be submitted in Canvas:
Assignments/Assignment 1.
Please do NOT submit other unnecessary files.